102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
import time
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import joblib
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import numpy as np
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import pytest
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from numpy.testing import assert_array_equal
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from sklearn import config_context, get_config
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from sklearn.compose import make_column_transformer
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from sklearn.datasets import load_iris
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import GridSearchCV
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn.utils.parallel import delayed, Parallel
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def get_working_memory():
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return get_config()["working_memory"]
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@pytest.mark.parametrize("n_jobs", [1, 2])
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@pytest.mark.parametrize("backend", ["loky", "threading", "multiprocessing"])
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def test_configuration_passes_through_to_joblib(n_jobs, backend):
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# Tests that the global global configuration is passed to joblib jobs
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with config_context(working_memory=123):
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results = Parallel(n_jobs=n_jobs, backend=backend)(
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delayed(get_working_memory)() for _ in range(2)
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)
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assert_array_equal(results, [123] * 2)
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def test_parallel_delayed_warnings():
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"""Informative warnings should be raised when mixing sklearn and joblib API"""
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# We should issue a warning when one wants to use sklearn.utils.fixes.Parallel
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# with joblib.delayed. The config will not be propagated to the workers.
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warn_msg = "`sklearn.utils.parallel.Parallel` needs to be used in conjunction"
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with pytest.warns(UserWarning, match=warn_msg) as records:
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Parallel()(joblib.delayed(time.sleep)(0) for _ in range(10))
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assert len(records) == 10
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# We should issue a warning if one wants to use sklearn.utils.fixes.delayed with
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# joblib.Parallel
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warn_msg = (
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"`sklearn.utils.parallel.delayed` should be used with "
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"`sklearn.utils.parallel.Parallel` to make it possible to propagate"
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)
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with pytest.warns(UserWarning, match=warn_msg) as records:
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joblib.Parallel()(delayed(time.sleep)(0) for _ in range(10))
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assert len(records) == 10
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@pytest.mark.parametrize("n_jobs", [1, 2])
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def test_dispatch_config_parallel(n_jobs):
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"""Check that we properly dispatch the configuration in parallel processing.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/issues/25239
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"""
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pd = pytest.importorskip("pandas")
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iris = load_iris(as_frame=True)
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class TransformerRequiredDataFrame(StandardScaler):
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def fit(self, X, y=None):
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assert isinstance(X, pd.DataFrame), "X should be a DataFrame"
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return super().fit(X, y)
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def transform(self, X, y=None):
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assert isinstance(X, pd.DataFrame), "X should be a DataFrame"
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return super().transform(X, y)
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dropper = make_column_transformer(
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("drop", [0]),
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remainder="passthrough",
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n_jobs=n_jobs,
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)
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param_grid = {"randomforestclassifier__max_depth": [1, 2, 3]}
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search_cv = GridSearchCV(
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make_pipeline(
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dropper,
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TransformerRequiredDataFrame(),
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RandomForestClassifier(n_estimators=5, n_jobs=n_jobs),
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),
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param_grid,
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cv=5,
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n_jobs=n_jobs,
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error_score="raise", # this search should not fail
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)
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# make sure that `fit` would fail in case we don't request dataframe
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with pytest.raises(AssertionError, match="X should be a DataFrame"):
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search_cv.fit(iris.data, iris.target)
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with config_context(transform_output="pandas"):
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# we expect each intermediate steps to output a DataFrame
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search_cv.fit(iris.data, iris.target)
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assert not np.isnan(search_cv.cv_results_["mean_test_score"]).any()
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